The present invention relates in general to diagnostic systems, and more particularly to an optimization method for scheduling an evaluation sequence of off-line alarm sources in a real-time diagnostic system.
In complex industrial processes, a computerized fault diagnostic system is frequently used to monitor alarms and detect possible sources of failure in the industrial process. Real-time fault diagnostic systems observe the operation of processes, detect the appearance and propagation of faults, and continuously update the list of possible fault causes to support on-line decision making for deciding whether to intervene in the process being monitored.
The ultimate purpose of the diagnostic system is to minimize the cost of the operation of industrial processes by finding all possible sources of detected process anomalies as early as possible, and by predicting the prospective impact of the faults on the operation of related process components. These techniques are particularly applicable in chemical and power engineering processes because of the extreme expense of down-time and the impact suffered as a result of a degradation of product quality.
A diagnostic system is frequently used to monitor extremely complex industrial operations, such as in a chemical or power plant. A typical complex industrial operation can have thousands of components performing hundreds of operations at any given time. Many of these operations are interdependent, and constantly interact with each other. The failure of any one component can potentially affect the performance of other operations that do not directly use the failed component. Therefore, a single component fault can effectively propagate to many other operations, and set off many different fault indicating alarms.
Alarms, which can be the output of low-level sensors or fault detection algorithms, are sent to the diagnostic system continuously. It is the job of the diagnostic system to receive the incoming alarms and provide a diagnosis of the facility according to the latest alarm combination.
In most systems, because of cost considerations, only a subset of the available alarm generating sources are on-line to the diagnostic system at any one time. Off-line alarm sources, either particular sensors or fault detection algorithms, are usually prohibitively expensive to operate continuously. Off-line alarm sources thus often include sensors that are not currently connected to the diagnostic computer and fault detection algorithms that are not currently operating. Therefore, only a subset of the possible failure modes indicated by the on-line alarms can be tested at any one time. Off-line alarm sources are read only when a specific request is made to do so by the processing system.
Previous systems employ both symptom-based and model-based categories of real-time diagnostic methods. Symptom-based diagnostic methods collect failure symptoms and try to match them with a particular symptom pattern which is characteristic of a possible failure cause. The symptom-failure cause association may be found using pattern recognition methods, deterministic reasoning, or probabilistic reasoning. The main disadvantages of the symptom-based diagnostic methods are that the association is highly dependent upon operational conditions, and that a reliable diagnosis requires the presence of well-developed symptoms, a condition which is not tolerable in most industrial applications. These disadvantages occur in symptom-based diagnostic systems in part because the number of possible symptoms that are caused by various failure nodes can be prohibitively large.
Model-based methods provide much better performance than symptom-based methods, but can only be used when detailed information is available about the structure of the system being monitored. In model-based methods, a model of the industrial process is generated prior to operation of the system, and is used during the diagnostic process to locate the possible failure sources. Different types of models, including quantitative models, qualitative models, and graph models, can be used in the diagnostics. The application of graph models in large scale systems has been most promising, mainly because of the predictable computation load of the applied graph algorithms.
Many graph model-based diagnostic methods are known in the prior art. However, because the graph model method requires a closed alarm set for the analysis of the industrial process, most of the techniques presently used can be applied to only off-line diagnostics. In these earlier systems, the whole process usually had to be restarted to update the diagnosis for new incoming alarms. In real-time situations, restarting the diagnostic process was computationally prohibitive.
It therefore an object of the present invention to provide a diagnostic system that continuously receives incoming alarms and calculates the criticality of off-line alarms according to the latest alarm combination. It also an object of the present invention to provide a diagnostic system that schedules particular alarm readings and failure detection methods to obtain the most relevant alarm data for a particular situation.